Global Stability Analysis of Neural Networks with Constant Time Delay via Frobenius Norm

Thoiyab N. Mohamed; Muruganantham P.; Rajchakit GrienggraiGunasekaran NallappanUnyong BunditHumphries UsaKaewmesri PrametLim Chee Peng

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Global Stability Analysis of Neural Networks with Constant Time Delay via Frobenius Norm

机译：Global Stability Analysis of Neural Networks with Constant Time Delay via Frobenius Norm

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This paper deals with the global asymptotic robust stability (GARS) of neural networks (NNs) with constant time delay via Frobenius norm. The Frobenius norm result has been utilized to find a new sufficient condition for the existence, uniqueness, and GARS of equilibrium point of the NNs. Some suitable Lyapunov functional and the slope bounded functions have been employed to find the new sufficient condition for GARS of NNs. Finally, we give some comparative study of numerical examples for explaining the advantageous of the proposed result along with the existing GARS results in terms of network parameters.

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《Mathematical Problems in Engineering: Theory, Methods and Applications》 |2020年第36期|4321312.1-4321312.14|共14页
作者
Thoiyab N. Mohamed; Muruganantham P.; Rajchakit GrienggraiGunasekaran NallappanUnyong BunditHumphries UsaKaewmesri PrametLim Chee Peng;
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Bharathidasan Univ, Jamal Mohamed Coll, Dept Math, Tiruchirappalli 620020, Tamil Nadu, India;

Maejo Univ, Fac Sci, Dept Math, Chiang Mai 50290, Thailand;

Shibaura Inst Technol, Dept Math Sci, Saitama 3378570, JapanPhuket Rajabhat Univ, Fac Sci & Technol, Dept Math, Phuket 83000, ThailandKing Mongkuts Univ Technol Thonburi KMUTT, Fac Sci, Dept Math, 126 Pracha Uthit Rd, Bang Mod 10140, Thung Khru, ThailandDeakin Univ, Inst Intelligent Syst Res & Innovat, Waurn Ponds, Vic 3216, Australia;

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Global Stability Analysis of Neural Networks with Constant Time Delay via Frobenius Norm

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